Transition-Aware best-of-N sampling for Longitudinal Chest X-ray Reports
Summary
This paper introduces transition-aware best-of-N sampling, a training-free method for generating longitudinal chest X-ray reports by encoding changes between prior and current examinations using set-to-set distance metrics.
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Paper page - Transition-Aware best-of-N sampling for Longitudinal Chest X-ray Reports
Source: https://huggingface.co/papers/2606.28393
Abstract
A novel training-free sampling method for chest X-ray report generation that leverages longitudinal patient history by encoding changes between prior and current examinations through set-to-set distance metrics.
In longitudinal clinical practice, every chest X-ray is read in the context of the patients prior exam, and much of what the radiologist communicates is the change from one visit to the next. To the best of our knowledge, we present the first training-freebest-of-N samplingscheme for pre-trained chest X-ray report generators that is explicitly aware of thislongitudinal priorto current transition. We call it transition-awarebest-of-N sampling, each report is split into sentences and embedded into an unordered set in Rd; each (prior, current) pair is reduced to a fixed-dimdirectional vectorvia aset-to-set distancedesigned to encode the change between the two sets; and candidates are scored bycosine distancefrom their candidatetransition vectorto a cached bank ofground-truth training transition vectors, aggregated asminorkNN. We instantiate the framework with four directional set distances (mean-shift, novelty residual, directed-Hausdorff anchor, and cost-weighted optimal transport) and evaluate on a multi-visitAP-PA cohort, running inference under three prompts on threevision-language generators. Transition-aware best-of-N outperforms random selection across the board, with the largest relative gains on the Impression section.
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